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I love this. Is it possible to give a feel of how this stacks up to the good old Opus 4.5 in coding quality? For me that was the turning point where agentic coding in Claude Code etc became usable. Have we hit that threshold?
Having played with it for like 3 hours now....I'm probably moving from CC to this
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What harness/quant did you use for testing?
nvfp4 mlx, literally barebones pi. edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
A lot of people are testing it, and reporting disappointed results / benchmaxxxing claim. But do not realize that thinking has a issue with the default configuration.
Important - make sure that THINKING is enabled. By default it wasn't although I was passing the flag --default-chat-template-kwargs '{"enable_thinking": true}' in vllm recipe. The generation_config.json file that is included has by default max_new_tokens as 32k which seems to be cutting off thinking altogether so increase it. At first I was very disappointed with the output I was seeing, but once thinking is enabled, the code quality seems to be MUCH better. More real world testing to be done.
https://www.reddit.com/r/LocalLLaMA/comments/1v2pg99/laguna_...
Looks impressive, and this size fits achievable home hardware. That said, if someone would kindly quantise this down for the 64GB paupers, that would be appreciated. (I know there’s likely degradation, but some people reported good results with a 2 bit version of Qwen 3.5 122B, and this is starting from a higher point. Would be interesting to try, at least.) Edit: someone in the process of doing so: https://huggingfa…
They have also published smaller 33B model called Laguna XS 2.1, its Q4 gguf is 20GB. https://huggingface.co/poolside/Laguna-XS-2.1-GGUF/tree/main
!! Be careful when testing the model. A lot of people are testing it, and reporting disappointed results / benchmaxxxing claim. But do not realize that thinking has a issue with the default configuration. Important - make sure that THINKING is enabled. By default it wasn't although I was passing the flag --default-chat-template-kwargs '{"enable_thinking": true}' in vllm recipe. The generation_config.json file that is…
Also, I really like Poolside's habit to compare not only to other top models in its weight class (others don't do it, looking at you Mistral), but also to the very top open-weight models, even much bigger ones like the 2.5T Kimi-K3!
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nvfp4 mlx, literally barebones pi. edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
What inference server are you using? They have a custom branch for llama.cpp, but I wouldn't be surprised at all if it still needs fixing.
Running deepseek flash on something locally now, this will have to wait a bit. I still stand by my initial quick assessment - looks capable. Some people on r/localllama also reported loops. We'll see in ~10 hours. Hopefully I haven't terribly mislead people.